Overview

Dataset statistics

Number of variables17
Number of observations584314
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.2 MiB
Average record size in memory144.0 B

Variable types

Numeric7
Categorical5
DateTime3
Text2

Alerts

item_id is highly overall correlated with Customer ID and 1 other fieldsHigh correlation
price is highly overall correlated with grand_totalHigh correlation
grand_total is highly overall correlated with priceHigh correlation
Customer ID is highly overall correlated with item_id and 1 other fieldsHigh correlation
status is highly overall correlated with BI StatusHigh correlation
BI Status is highly overall correlated with statusHigh correlation
Year is highly overall correlated with item_id and 1 other fieldsHigh correlation
status is highly imbalanced (51.7%)Imbalance
qty_ordered is highly skewed (γ1 = 185.8821021)Skewed
grand_total is highly skewed (γ1 = 254.9541717)Skewed
item_id has unique valuesUnique
grand_total has 9616 (1.6%) zerosZeros
discount_amount has 376121 (64.4%) zerosZeros

Reproduction

Analysis started2023-09-19 08:21:24.025894
Analysis finished2023-09-19 08:23:03.421173
Duration1 minute and 39.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

item_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct584314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean565590.35
Minimum211131
Maximum905208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2023-09-19T11:23:07.085702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum211131
5-th percentile247513.65
Q1394945.25
median568301
Q3738981.75
95-th percentile872508.35
Maximum905208
Range694077
Interquartile range (IQR)344036.5

Descriptive statistics

Standard deviation200101.17
Coefficient of variation (CV)0.35379171
Kurtosis-1.184214
Mean565590.35
Median Absolute Deviation (MAD)171855.5
Skewness-0.047040402
Sum3.3048236 × 1011
Variance4.0040479 × 1010
MonotonicityNot monotonic
2023-09-19T11:23:07.981188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211131 1
 
< 0.1%
682904 1
 
< 0.1%
682902 1
 
< 0.1%
682889 1
 
< 0.1%
682890 1
 
< 0.1%
682892 1
 
< 0.1%
682894 1
 
< 0.1%
682896 1
 
< 0.1%
682905 1
 
< 0.1%
682900 1
 
< 0.1%
Other values (584304) 584304
> 99.9%
ValueCountFrequency (%)
211131 1
< 0.1%
211133 1
< 0.1%
211134 1
< 0.1%
211135 1
< 0.1%
211136 1
< 0.1%
211137 1
< 0.1%
211138 1
< 0.1%
211139 1
< 0.1%
211140 1
< 0.1%
211141 1
< 0.1%
ValueCountFrequency (%)
905208 1
< 0.1%
905207 1
< 0.1%
905206 1
< 0.1%
905205 1
< 0.1%
905204 1
< 0.1%
905202 1
< 0.1%
905200 1
< 0.1%
905199 1
< 0.1%
905198 1
< 0.1%
905196 1
< 0.1%

status
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
complete
233684 
canceled
201127 
received
77284 
order_refunded
59498 
refund
 
8020
Other values (11)
 
4701

Length

Max length14
Median length8
Mean length8.549944
Min length2

Characters and Unicode

Total characters4995852
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcomplete
2nd rowcanceled
3rd rowcanceled
4th rowcomplete
5th roworder_refunded

Common Values

ValueCountFrequency (%)
complete 233684
40.0%
canceled 201127
34.4%
received 77284
 
13.2%
order_refunded 59498
 
10.2%
refund 8020
 
1.4%
cod 2854
 
0.5%
paid 1159
 
0.2%
closed 494
 
0.1%
payment_review 57
 
< 0.1%
pending 48
 
< 0.1%
Other values (6) 89
 
< 0.1%

Length

2023-09-19T11:23:08.968848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
complete 233684
40.0%
canceled 201127
34.4%
received 77284
 
13.2%
order_refunded 59498
 
10.2%
refund 8020
 
1.4%
cod 2854
 
0.5%
paid 1159
 
0.2%
closed 494
 
0.1%
payment_review 57
 
< 0.1%
pending 48
 
< 0.1%
Other values (6) 89
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 1288780
25.8%
c 716607
14.3%
d 469559
 
9.4%
l 435343
 
8.7%
o 296594
 
5.9%
n 268849
 
5.4%
r 263898
 
5.3%
p 235002
 
4.7%
m 233741
 
4.7%
t 233741
 
4.7%
Other values (14) 553738
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4936282
98.8%
Connector Punctuation 59562
 
1.2%
Other Punctuation 4
 
< 0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1288780
26.1%
c 716607
14.5%
d 469559
 
9.5%
l 435343
 
8.8%
o 296594
 
6.0%
n 268849
 
5.4%
r 263898
 
5.3%
p 235002
 
4.8%
m 233741
 
4.7%
t 233741
 
4.7%
Other values (11) 494168
 
10.0%
Connector Punctuation
ValueCountFrequency (%)
_ 59562
100.0%
Other Punctuation
ValueCountFrequency (%)
\ 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4936286
98.8%
Common 59566
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1288780
26.1%
c 716607
14.5%
d 469559
 
9.5%
l 435343
 
8.8%
o 296594
 
6.0%
n 268849
 
5.4%
r 263898
 
5.3%
p 235002
 
4.8%
m 233741
 
4.7%
t 233741
 
4.7%
Other values (12) 494172
 
10.0%
Common
ValueCountFrequency (%)
_ 59562
> 99.9%
\ 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4995852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1288780
25.8%
c 716607
14.3%
d 469559
 
9.4%
l 435343
 
8.7%
o 296594
 
5.9%
n 268849
 
5.4%
r 263898
 
5.3%
p 235002
 
4.7%
m 233741
 
4.7%
t 233741
 
4.7%
Other values (14) 553738
11.1%
Distinct789
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Minimum2016-07-01 00:00:00
Maximum2018-08-28 00:00:00
2023-09-19T11:23:09.806367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:23:10.635894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sku
Text

Distinct84869
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
2023-09-19T11:23:13.262346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length69
Median length63
Mean length20.254076
Min length5

Characters and Unicode

Total characters11834740
Distinct characters94
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38636 ?
Unique (%)6.6%

Sample

1st rowkreations_YI 06-L
2nd rowkcc_Buy 2 Frey Air Freshener & Get 1 Kasual Body Spray Free
3rd rowEgo_UP0017-999-MR0
4th rowkcc_krone deal
5th rowBK7010400AG
ValueCountFrequency (%)
17805
 
1.9%
infinix 5978
 
0.7%
halwa 5418
 
0.6%
hot 4859
 
0.5%
black 4616
 
0.5%
one 4385
 
0.5%
of 4104
 
0.4%
matsam59db75adb2f80 3775
 
0.4%
sohan 3718
 
0.4%
al 3661
 
0.4%
Other values (86302) 856460
93.6%
2023-09-19T11:23:15.098611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 765671
 
6.5%
5 536681
 
4.5%
0 464114
 
3.9%
B 423956
 
3.6%
- 366666
 
3.1%
e 348339
 
2.9%
9 347767
 
2.9%
1 345299
 
2.9%
a 344863
 
2.9%
E 340559
 
2.9%
Other values (84) 7550825
63.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4515531
38.2%
Decimal Number 3266333
27.6%
Lowercase Letter 3026234
25.6%
Dash Punctuation 366685
 
3.1%
Space Separator 336852
 
2.8%
Connector Punctuation 297722
 
2.5%
Other Punctuation 14679
 
0.1%
Open Punctuation 4520
 
< 0.1%
Close Punctuation 4506
 
< 0.1%
Math Symbol 1525
 
< 0.1%
Other values (3) 153
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 348339
11.5%
a 344863
11.4%
i 247921
 
8.2%
o 208073
 
6.9%
l 201562
 
6.7%
r 200688
 
6.6%
n 195456
 
6.5%
t 166925
 
5.5%
s 143907
 
4.8%
c 121305
 
4.0%
Other values (18) 847195
28.0%
Uppercase Letter
ValueCountFrequency (%)
A 765671
17.0%
B 423956
 
9.4%
E 340559
 
7.5%
C 327375
 
7.2%
F 322824
 
7.1%
D 288831
 
6.4%
M 276375
 
6.1%
S 231828
 
5.1%
T 191223
 
4.2%
P 175894
 
3.9%
Other values (17) 1170995
25.9%
Decimal Number
ValueCountFrequency (%)
5 536681
16.4%
0 464114
14.2%
9 347767
10.6%
1 345299
10.6%
2 281020
8.6%
3 274323
8.4%
7 271621
8.3%
4 262124
8.0%
6 242793
7.4%
8 240591
7.4%
Other Punctuation
ValueCountFrequency (%)
& 5968
40.7%
. 3473
23.7%
/ 1987
 
13.5%
' 1174
 
8.0%
, 922
 
6.3%
# 392
 
2.7%
" 280
 
1.9%
: 251
 
1.7%
% 231
 
1.6%
\ 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 1502
98.5%
| 12
 
0.8%
= 9
 
0.6%
× 2
 
0.1%
Control
ValueCountFrequency (%)
12
46.2%
12
46.2%
2
 
7.7%
Dash Punctuation
ValueCountFrequency (%)
- 366666
> 99.9%
– 19
 
< 0.1%
Space Separator
ValueCountFrequency (%)
336309
99.8%
  543
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 4495
99.4%
[ 25
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 4481
99.4%
] 25
 
0.6%
Other Symbol
ValueCountFrequency (%)
° 3
75.0%
® 1
 
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 297722
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7541765
63.7%
Common 4292975
36.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 765671
 
10.2%
B 423956
 
5.6%
e 348339
 
4.6%
a 344863
 
4.6%
E 340559
 
4.5%
C 327375
 
4.3%
F 322824
 
4.3%
D 288831
 
3.8%
M 276375
 
3.7%
i 247921
 
3.3%
Other values (45) 3855051
51.1%
Common
ValueCountFrequency (%)
5 536681
12.5%
0 464114
10.8%
- 366666
8.5%
9 347767
8.1%
1 345299
8.0%
336309
7.8%
_ 297722
 
6.9%
2 281020
 
6.5%
3 274323
 
6.4%
7 271621
 
6.3%
Other values (29) 771453
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11834127
> 99.9%
None 594
 
< 0.1%
Punctuation 19
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 765671
 
6.5%
5 536681
 
4.5%
0 464114
 
3.9%
B 423956
 
3.6%
- 366666
 
3.1%
e 348339
 
2.9%
9 347767
 
2.9%
1 345299
 
2.9%
a 344863
 
2.9%
E 340559
 
2.9%
Other values (76) 7550212
63.8%
None
ValueCountFrequency (%)
  543
91.4%
è 20
 
3.4%
È 13
 
2.2%
é 12
 
2.0%
° 3
 
0.5%
× 2
 
0.3%
® 1
 
0.2%
Punctuation
ValueCountFrequency (%)
– 19
100.0%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct9119
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6350.7663
Minimum0
Maximum1012625.9
Zeros2214
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2023-09-19T11:23:15.656774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile95
Q1360
median899.4
Q34090
95-th percentile29199
Maximum1012625.9
Range1012625.9
Interquartile range (IQR)3730

Descriptive statistics

Standard deviation14951.495
Coefficient of variation (CV)2.354282
Kurtosis71.228003
Mean6350.7663
Median Absolute Deviation (MAD)699.4
Skewness5.2278765
Sum3.7108417 × 109
Variance2.235472 × 108
MonotonicityNot monotonic
2023-09-19T11:23:16.216206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 11077
 
1.9%
999 10119
 
1.7%
500 9213
 
1.6%
699 8407
 
1.4%
499 7693
 
1.3%
399 6999
 
1.2%
299 6647
 
1.1%
799 6024
 
1.0%
599 6008
 
1.0%
899 4471
 
0.8%
Other values (9109) 507656
86.9%
ValueCountFrequency (%)
0 2214
0.4%
0.1 15
 
< 0.1%
0.11 8
 
< 0.1%
0.15 1
 
< 0.1%
0.2 16
 
< 0.1%
0.8 9
 
< 0.1%
1 1237
0.2%
1.3 24
 
< 0.1%
1.5 20
 
< 0.1%
1.6 34
 
< 0.1%
ValueCountFrequency (%)
1012625.9 1
 
< 0.1%
515975 1
 
< 0.1%
479000 4
< 0.1%
330499 2
< 0.1%
320000 1
 
< 0.1%
307970 2
< 0.1%
300000 4
< 0.1%
291667 2
< 0.1%
289999 1
 
< 0.1%
265499 1
 
< 0.1%

qty_ordered
Real number (ℝ)

SKEWED 

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2943075
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2023-09-19T11:23:16.769682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum1000
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9881503
Coefficient of variation (CV)3.0813004
Kurtosis42941.241
Mean1.2943075
Median Absolute Deviation (MAD)0
Skewness185.8821
Sum756282
Variance15.905343
MonotonicityNot monotonic
2023-09-19T11:23:17.240157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 505125
86.4%
2 46607
 
8.0%
3 15404
 
2.6%
5 11202
 
1.9%
4 4139
 
0.7%
6 588
 
0.1%
10 291
 
< 0.1%
8 153
 
< 0.1%
7 112
 
< 0.1%
12 95
 
< 0.1%
Other values (63) 598
 
0.1%
ValueCountFrequency (%)
1 505125
86.4%
2 46607
 
8.0%
3 15404
 
2.6%
4 4139
 
0.7%
5 11202
 
1.9%
6 588
 
0.1%
7 112
 
< 0.1%
8 153
 
< 0.1%
9 42
 
< 0.1%
10 291
 
< 0.1%
ValueCountFrequency (%)
1000 6
< 0.1%
502 1
 
< 0.1%
500 4
< 0.1%
380 1
 
< 0.1%
304 1
 
< 0.1%
300 2
 
< 0.1%
200 7
< 0.1%
187 1
 
< 0.1%
186 1
 
< 0.1%
185 1
 
< 0.1%

grand_total
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct36828
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8532.8924
Minimum-1594
Maximum17888000
Zeros9616
Zeros (%)1.6%
Negative76
Negative (%)< 0.1%
Memory size8.9 MiB
2023-09-19T11:23:17.683245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1594
5-th percentile160
Q1945.2
median1961
Q36999
95-th percentile34499
Maximum17888000
Range17889594
Interquartile range (IQR)6053.8

Descriptive statistics

Standard deviation61331.682
Coefficient of variation (CV)7.1876779
Kurtosis74165.691
Mean8532.8924
Median Absolute Deviation (MAD)1462
Skewness254.95417
Sum4.9858885 × 109
Variance3.7615752 × 109
MonotonicityNot monotonic
2023-09-19T11:23:18.255206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9616
 
1.6%
1000 5145
 
0.9%
2000 3780
 
0.6%
2500 3235
 
0.6%
5000 2912
 
0.5%
1149 2524
 
0.4%
12599 2520
 
0.4%
399 2459
 
0.4%
4000 2387
 
0.4%
150 2238
 
0.4%
Other values (36818) 547498
93.7%
ValueCountFrequency (%)
-1594 1
 
< 0.1%
-1311.5 7
< 0.1%
-1106.65 1
 
< 0.1%
-873.4 1
 
< 0.1%
-528 1
 
< 0.1%
-511 2
 
< 0.1%
-425.7 2
 
< 0.1%
-384 1
 
< 0.1%
-340.6 16
< 0.1%
-249 1
 
< 0.1%
ValueCountFrequency (%)
17888000 6
< 0.1%
1315875 1
 
< 0.1%
1280473 2
 
< 0.1%
1279980 1
 
< 0.1%
1155966 1
 
< 0.1%
1039479 1
 
< 0.1%
1028751 4
< 0.1%
1012625.9 1
 
< 0.1%
888065 7
< 0.1%
847024 7
< 0.1%

category_name_1
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Mobiles & Tablets
115709 
Men's Fashion
92218 
Women's Fashion
59720 
Appliances
52413 
Superstore
43611 
Other values (11)
220643 

Length

Max length18
Median length17
Mean length12.839496
Min length2

Characters and Unicode

Total characters7502297
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWomen's Fashion
2nd rowBeauty & Grooming
3rd rowWomen's Fashion
4th rowBeauty & Grooming
5th rowSoghaat

Common Values

ValueCountFrequency (%)
Mobiles & Tablets 115709
19.8%
Men's Fashion 92218
15.8%
Women's Fashion 59720
10.2%
Appliances 52413
9.0%
Superstore 43611
 
7.5%
Beauty & Grooming 41493
 
7.1%
Soghaat 34011
 
5.8%
Others 29208
 
5.0%
Home & Living 26504
 
4.5%
Entertainment 26322
 
4.5%
Other values (6) 63105
10.8%

Length

2023-09-19T11:23:18.851847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
221175
18.8%
fashion 151938
12.9%
mobiles 115709
9.8%
tablets 115709
9.8%
men's 92218
 
7.8%
women's 59720
 
5.1%
appliances 52413
 
4.4%
superstore 43611
 
3.7%
beauty 41493
 
3.5%
grooming 41493
 
3.5%
Other values (14) 243123
20.6%

Most occurring characters

ValueCountFrequency (%)
s 696387
 
9.3%
e 690342
 
9.2%
594288
 
7.9%
o 562088
 
7.5%
n 522663
 
7.0%
a 493366
 
6.6%
i 476783
 
6.4%
t 397413
 
5.3%
l 304811
 
4.1%
b 247907
 
3.3%
Other values (29) 2516249
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5569636
74.2%
Uppercase Letter 957427
 
12.8%
Space Separator 594288
 
7.9%
Other Punctuation 380946
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 696387
12.5%
e 690342
12.4%
o 562088
10.1%
n 522663
9.4%
a 493366
8.9%
i 476783
8.6%
t 397413
7.1%
l 304811
 
5.5%
b 247907
 
4.5%
h 236137
 
4.2%
Other values (10) 941739
16.9%
Uppercase Letter
ValueCountFrequency (%)
M 207927
21.7%
F 151938
15.9%
T 115709
12.1%
S 98602
10.3%
B 59852
 
6.3%
W 59720
 
6.2%
A 52413
 
5.5%
H 44006
 
4.6%
G 41493
 
4.3%
E 29800
 
3.1%
Other values (5) 95967
10.0%
Other Punctuation
ValueCountFrequency (%)
& 221175
58.1%
' 151938
39.9%
\ 7833
 
2.1%
Space Separator
ValueCountFrequency (%)
594288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6527063
87.0%
Common 975234
 
13.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 696387
 
10.7%
e 690342
 
10.6%
o 562088
 
8.6%
n 522663
 
8.0%
a 493366
 
7.6%
i 476783
 
7.3%
t 397413
 
6.1%
l 304811
 
4.7%
b 247907
 
3.8%
h 236137
 
3.6%
Other values (25) 1899166
29.1%
Common
ValueCountFrequency (%)
594288
60.9%
& 221175
 
22.7%
' 151938
 
15.6%
\ 7833
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7502297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 696387
 
9.3%
e 690342
 
9.2%
594288
 
7.9%
o 562088
 
7.5%
n 522663
 
7.0%
a 493366
 
6.6%
i 476783
 
6.4%
t 397413
 
5.3%
l 304811
 
4.1%
b 247907
 
3.3%
Other values (29) 2516249
33.5%

discount_amount
Real number (ℝ)

ZEROS 

Distinct28058
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.65533
Minimum-599.5
Maximum90300
Zeros376121
Zeros (%)64.4%
Negative3
Negative (%)< 0.1%
Memory size8.9 MiB
2023-09-19T11:23:19.372707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-599.5
5-th percentile0
Q10
median0
Q3160.7344
95-th percentile2930
Maximum90300
Range90899.5
Interquartile range (IQR)160.7344

Descriptive statistics

Standard deviation1507.1858
Coefficient of variation (CV)3.016451
Kurtosis117.52989
Mean499.65533
Median Absolute Deviation (MAD)0
Skewness6.8412944
Sum2.919556 × 108
Variance2271609.1
MonotonicityNot monotonic
2023-09-19T11:23:19.997350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 376121
64.4%
1000 5420
 
0.9%
2000 4143
 
0.7%
500 3648
 
0.6%
200 3495
 
0.6%
3000 2361
 
0.4%
7000 2289
 
0.4%
1500 1625
 
0.3%
4000 1429
 
0.2%
2200 1409
 
0.2%
Other values (28048) 182374
31.2%
ValueCountFrequency (%)
-599.5 1
 
< 0.1%
-2 2
 
< 0.1%
0 376121
64.4%
0.08 1
 
< 0.1%
0.1 6
 
< 0.1%
0.14 1
 
< 0.1%
0.15 36
 
< 0.1%
0.16 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 12
 
< 0.1%
ValueCountFrequency (%)
90300 2
 
< 0.1%
50355.25 5
< 0.1%
50127.75 5
< 0.1%
48205 1
 
< 0.1%
47500 4
< 0.1%
45000 1
 
< 0.1%
42498.75 1
 
< 0.1%
41885 1
 
< 0.1%
38075.07 8
< 0.1%
35609.39 1
 
< 0.1%

payment_method
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
cod
271770 
Payaxis
97640 
Easypay
82893 
jazzwallet
35145 
easypay_voucher
31175 
Other values (13)
65691 

Length

Max length17
Median length16
Mean length6.1909727
Min length3

Characters and Unicode

Total characters3617472
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcod
2nd rowcod
3rd rowcod
4th rowcod
5th rowcod

Common Values

ValueCountFrequency (%)
cod 271770
46.5%
Payaxis 97640
 
16.7%
Easypay 82893
 
14.2%
jazzwallet 35145
 
6.0%
easypay_voucher 31175
 
5.3%
bankalfalah 23057
 
3.9%
jazzvoucher 15633
 
2.7%
Easypay_MA 14026
 
2.4%
customercredit 7554
 
1.3%
apg 1758
 
0.3%
Other values (8) 3663
 
0.6%

Length

2023-09-19T11:23:20.561211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cod 271770
46.5%
payaxis 97640
 
16.7%
easypay 82893
 
14.2%
jazzwallet 35145
 
6.0%
easypay_voucher 31175
 
5.3%
bankalfalah 23057
 
3.9%
jazzvoucher 15633
 
2.7%
easypay_ma 14026
 
2.4%
customercredit 7554
 
1.3%
apg 1758
 
0.3%
Other values (8) 3663
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 635593
17.6%
y 355166
 
9.8%
c 337170
 
9.3%
o 327721
 
9.1%
d 282070
 
7.8%
s 234812
 
6.5%
e 132551
 
3.7%
p 130754
 
3.6%
l 118024
 
3.3%
i 107928
 
3.0%
Other values (20) 955683
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3349660
92.6%
Uppercase Letter 222611
 
6.2%
Connector Punctuation 45201
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 635593
19.0%
y 355166
10.6%
c 337170
10.1%
o 327721
9.8%
d 282070
8.4%
s 234812
 
7.0%
e 132551
 
4.0%
p 130754
 
3.9%
l 118024
 
3.5%
i 107928
 
3.2%
Other values (15) 687871
20.5%
Uppercase Letter
ValueCountFrequency (%)
P 97640
43.9%
E 96919
43.5%
M 14026
 
6.3%
A 14026
 
6.3%
Connector Punctuation
ValueCountFrequency (%)
_ 45201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3572271
98.8%
Common 45201
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 635593
17.8%
y 355166
 
9.9%
c 337170
 
9.4%
o 327721
 
9.2%
d 282070
 
7.9%
s 234812
 
6.6%
e 132551
 
3.7%
p 130754
 
3.7%
l 118024
 
3.3%
i 107928
 
3.0%
Other values (19) 910482
25.5%
Common
ValueCountFrequency (%)
_ 45201
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3617472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 635593
17.6%
y 355166
 
9.8%
c 337170
 
9.3%
o 327721
 
9.1%
d 282070
 
7.8%
s 234812
 
6.5%
e 132551
 
3.7%
p 130754
 
3.6%
l 118024
 
3.3%
i 107928
 
3.0%
Other values (20) 955683
26.4%

BI Status
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Net
234177 
Gross
201317 
Valid
148819 
#REF!
 
1

Length

Max length5
Median length5
Mean length4.1984549
Min length3

Characters and Unicode

Total characters2453216
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row#REF!
2nd rowGross
3rd rowGross
4th rowNet
5th rowValid

Common Values

ValueCountFrequency (%)
Net 234177
40.1%
Gross 201317
34.5%
Valid 148819
25.5%
#REF! 1
 
< 0.1%

Length

2023-09-19T11:23:21.142061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T11:23:21.966255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
net 234177
40.1%
gross 201317
34.5%
valid 148819
25.5%
ref 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s 402634
16.4%
N 234177
9.5%
t 234177
9.5%
e 234177
9.5%
G 201317
8.2%
r 201317
8.2%
o 201317
8.2%
i 148819
 
6.1%
d 148819
 
6.1%
a 148819
 
6.1%
Other values (7) 297643
12.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1868898
76.2%
Uppercase Letter 584316
 
23.8%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 402634
21.5%
t 234177
12.5%
e 234177
12.5%
r 201317
10.8%
o 201317
10.8%
i 148819
 
8.0%
d 148819
 
8.0%
a 148819
 
8.0%
l 148819
 
8.0%
Uppercase Letter
ValueCountFrequency (%)
N 234177
40.1%
G 201317
34.5%
V 148819
25.5%
R 1
 
< 0.1%
E 1
 
< 0.1%
F 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
# 1
50.0%
! 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2453214
> 99.9%
Common 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 402634
16.4%
N 234177
9.5%
t 234177
9.5%
e 234177
9.5%
G 201317
8.2%
r 201317
8.2%
o 201317
8.2%
i 148819
 
6.1%
d 148819
 
6.1%
a 148819
 
6.1%
Other values (5) 297641
12.1%
Common
ValueCountFrequency (%)
# 1
50.0%
! 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2453216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 402634
16.4%
N 234177
9.5%
t 234177
9.5%
e 234177
9.5%
G 201317
8.2%
r 201317
8.2%
o 201317
8.2%
i 148819
 
6.1%
d 148819
 
6.1%
a 148819
 
6.1%
Other values (7) 297643
12.1%

MV
Text

Distinct9720
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
2023-09-19T11:23:23.228122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length7
Mean length4.144573
Min length1

Characters and Unicode

Total characters2421732
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2234 ?
Unique (%)0.4%

Sample

1st row1,950
2nd row240
3rd row2,450
4th row360
5th row1,110
ValueCountFrequency (%)
999 9515
 
1.6%
699 7801
 
1.3%
499 7157
 
1.2%
1,000 6894
 
1.2%
399 6506
 
1.1%
299 5997
 
1.0%
599 5721
 
1.0%
799 5715
 
1.0%
2,000 4498
 
0.8%
899 4353
 
0.7%
Other values (9710) 520157
89.0%
2023-09-19T11:23:24.834928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 414270
17.1%
9 399021
16.5%
, 289292
11.9%
1 247183
10.2%
5 220100
9.1%
2 188201
7.8%
4 151871
 
6.3%
3 146602
 
6.1%
8 128584
 
5.3%
6 117159
 
4.8%
Other values (3) 119449
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2121370
87.6%
Other Punctuation 289292
 
11.9%
Space Separator 8856
 
0.4%
Dash Punctuation 2214
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 414270
19.5%
9 399021
18.8%
1 247183
11.7%
5 220100
10.4%
2 188201
8.9%
4 151871
 
7.2%
3 146602
 
6.9%
8 128584
 
6.1%
6 117159
 
5.5%
7 108379
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 289292
100.0%
Space Separator
ValueCountFrequency (%)
8856
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2214
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2421732
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 414270
17.1%
9 399021
16.5%
, 289292
11.9%
1 247183
10.2%
5 220100
9.1%
2 188201
7.8%
4 151871
 
6.3%
3 146602
 
6.1%
8 128584
 
5.3%
6 117159
 
4.8%
Other values (3) 119449
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2421732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 414270
17.1%
9 399021
16.5%
, 289292
11.9%
1 247183
10.2%
5 220100
9.1%
2 188201
7.8%
4 151871
 
6.3%
3 146602
 
6.1%
8 128584
 
5.3%
6 117159
 
4.8%
Other values (3) 119449
 
4.9%
Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Minimum2016-07-01 00:00:00
Maximum2018-08-01 00:00:00
2023-09-19T11:23:25.346188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:23:25.906724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

Customer ID
Real number (ℝ)

HIGH CORRELATION 

Distinct115304
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45779.112
Minimum1
Maximum115326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2023-09-19T11:23:26.737051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile546
Q113511
median42850
Q373513.75
95-th percentile107029
Maximum115326
Range115325
Interquartile range (IQR)60002.75

Descriptive statistics

Standard deviation34411.486
Coefficient of variation (CV)0.75168531
Kurtosis-1.125135
Mean45779.112
Median Absolute Deviation (MAD)29886
Skewness0.32681856
Sum2.6749376 × 1010
Variance1.1841504 × 109
MonotonicityNot monotonic
2023-09-19T11:23:27.357999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85775 2524
 
0.4%
163 2349
 
0.4%
35 1877
 
0.3%
33 1397
 
0.2%
31025 1369
 
0.2%
806 1310
 
0.2%
1404 1269
 
0.2%
767 1234
 
0.2%
820 1190
 
0.2%
58 1182
 
0.2%
Other values (115294) 568613
97.3%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
3 5
 
< 0.1%
4 428
0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
115326 1
 
< 0.1%
115325 2
< 0.1%
115324 1
 
< 0.1%
115323 1
 
< 0.1%
115322 2
< 0.1%
115321 1
 
< 0.1%
115320 3
< 0.1%
115319 3
< 0.1%
115318 1
 
< 0.1%
115317 1
 
< 0.1%

Year
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
2017
290908 
2018
159505 
2016
133901 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2337256
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2017 290908
49.8%
2018 159505
27.3%
2016 133901
22.9%

Length

2023-09-19T11:23:27.892246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T11:23:28.336057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2017 290908
49.8%
2018 159505
27.3%
2016 133901
22.9%

Most occurring characters

ValueCountFrequency (%)
2 584314
25.0%
0 584314
25.0%
1 584314
25.0%
7 290908
12.4%
8 159505
 
6.8%
6 133901
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2337256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 584314
25.0%
0 584314
25.0%
1 584314
25.0%
7 290908
12.4%
8 159505
 
6.8%
6 133901
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2337256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 584314
25.0%
0 584314
25.0%
1 584314
25.0%
7 290908
12.4%
8 159505
 
6.8%
6 133901
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2337256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 584314
25.0%
0 584314
25.0%
1 584314
25.0%
7 290908
12.4%
8 159505
 
6.8%
6 133901
 
5.7%

Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1687261
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 MiB
2023-09-19T11:23:28.642106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median7
Q311
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.4862632
Coefficient of variation (CV)0.48631559
Kurtosis-1.3723719
Mean7.1687261
Median Absolute Deviation (MAD)4
Skewness-0.19349323
Sum4188787
Variance12.154031
MonotonicityNot monotonic
2023-09-19T11:23:28.951500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 155453
26.6%
5 62571
10.7%
3 61470
 
10.5%
8 48512
 
8.3%
7 39146
 
6.7%
2 38763
 
6.6%
6 34526
 
5.9%
4 33985
 
5.8%
10 30619
 
5.2%
12 29196
 
5.0%
Other values (2) 50073
 
8.6%
ValueCountFrequency (%)
1 26050
4.5%
2 38763
6.6%
3 61470
10.5%
4 33985
5.8%
5 62571
10.7%
6 34526
5.9%
7 39146
6.7%
8 48512
8.3%
9 24023
 
4.1%
10 30619
5.2%
ValueCountFrequency (%)
12 29196
 
5.0%
11 155453
26.6%
10 30619
 
5.2%
9 24023
 
4.1%
8 48512
 
8.3%
7 39146
 
6.7%
6 34526
 
5.9%
5 62571
10.7%
4 33985
 
5.8%
3 61470
 
10.5%

M-Y
Date

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Minimum2016-07-01 00:00:00
Maximum2018-08-01 00:00:00
2023-09-19T11:23:29.300520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:23:29.645239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

Interactions

2023-09-19T11:22:47.032281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:18.804182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:24.232642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:28.845001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:33.537297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:38.975147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:43.317009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:47.628019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:19.722225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:25.041921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:29.360738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:34.416785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:39.671652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:43.888980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:48.268281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:20.464690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:25.715137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:30.249186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:35.116840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:40.317029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:44.485923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:48.867451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:21.176952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:26.418462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:30.824018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:35.740017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:41.083353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:45.001963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:49.530198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:21.994036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:27.121626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:31.481157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:36.486359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:41.765119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:45.549737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:50.147199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:22.706504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:27.822062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:32.141112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:37.201501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:42.277143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:46.024547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:50.806278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:23.471982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:28.354129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:32.875133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:38.200060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:42.804066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T11:22:46.541701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-19T11:23:30.032587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
item_idpriceqty_orderedgrand_totaldiscount_amountCustomer IDMonthstatuscategory_name_1payment_methodBI StatusYear
item_id1.0000.1790.1680.2940.1440.767-0.2750.1530.1940.2890.2130.927
price0.1791.000-0.1620.7300.2820.221-0.0570.0280.0450.0700.0390.052
qty_ordered0.168-0.1621.0000.058-0.0490.126-0.1580.0010.0050.0070.0020.004
grand_total0.2940.7300.0581.0000.2470.277-0.1130.0000.0040.0120.0010.003
discount_amount0.1440.282-0.0490.2471.0000.0990.0570.0160.0470.0660.0200.009
Customer ID0.7670.2210.1260.2770.0991.000-0.2060.1050.2090.2220.1540.673
Month-0.275-0.057-0.158-0.1130.057-0.2061.0000.1110.1680.1760.1550.486
status0.1530.0280.0010.0000.0160.1050.1111.0000.0830.1540.8160.225
category_name_10.1940.0450.0050.0040.0470.2090.1680.0831.0000.1350.1600.271
payment_method0.2890.0700.0070.0120.0660.2220.1760.1540.1351.0000.3310.359
BI Status0.2130.0390.0020.0010.0200.1540.1550.8160.1600.3311.0000.180
Year0.9270.0520.0040.0030.0090.6730.4860.2250.2710.3590.1801.000

Missing values

2023-09-19T11:22:52.844658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-19T11:22:56.836539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

item_idstatuscreated_atskupriceqty_orderedgrand_totalcategory_name_1discount_amountpayment_methodBI StatusMVCustomer SinceCustomer IDYearMonthM-Y
0211131complete2016-07-01kreations_YI 06-L1950.011950.0Women's Fashion0.0cod#REF!1,9502016-07-011.0201672016-07-01
1211133canceled2016-07-01kcc_Buy 2 Frey Air Freshener & Get 1 Kasual Body Spray Free240.01240.0Beauty & Grooming0.0codGross2402016-07-012.0201672016-07-01
2211134canceled2016-07-01Ego_UP0017-999-MR02450.012450.0Women's Fashion0.0codGross2,4502016-07-013.0201672016-07-01
3211135complete2016-07-01kcc_krone deal360.0160.0Beauty & Grooming300.0codNet3602016-07-014.0201672016-07-01
4211136order_refunded2016-07-01BK7010400AG555.021110.0Soghaat0.0codValid1,1102016-07-015.0201672016-07-01
5211137canceled2016-07-01UK_Namkino All In One 200 Gms80.0180.0Soghaat0.0codGross802016-07-016.0201672016-07-01
6211138complete2016-07-01kcc_krone deal360.0160.0Beauty & Grooming300.0codNet3602016-07-017.0201672016-07-01
7211139complete2016-07-01UK_Namkino Mix Nimco 400 Gms170.01170.0Soghaat0.0codNet1702016-07-016.0201672016-07-01
8211140canceled2016-07-01Apple iPhone 6S 64GB96499.0196499.0Mobiles & Tablets0.0ublcreditcardGross96,4992016-07-018.0201672016-07-01
9211141canceled2016-07-01Apple iPhone 6S 64GB96499.0196499.0Mobiles & Tablets0.0mygatewayGross96,4992016-07-018.0201672016-07-01
item_idstatuscreated_atskupriceqty_orderedgrand_totalcategory_name_1discount_amountpayment_methodBI StatusMVCustomer SinceCustomer IDYearMonthM-Y
584514905196paid2018-08-28MEFGUL5A9F882AA5B99-361299.010.0Men's Fashion0.0customercreditValid1,2992018-06-01111132.0201882018-08-01
584515905198paid2018-08-28MEFPAK5B360B03C6B72999.010.0Men's Fashion0.0customercreditValid9992018-06-01111132.0201882018-08-01
584516905199pending2018-08-28MATINF59BAB39FDBEF16760.0213770.0Mobiles & Tablets0.0jazzvoucherGross13,5202016-09-018123.0201882018-08-01
584517905200cod2018-08-28WOFVAL59D5EA84167F9-M400.01550.0Women's Fashion0.0codValid4002018-08-01115325.0201882018-08-01
584518905202cod2018-08-28WOFNIG5B4D7EB0E9FDD-L499.01649.0Women's Fashion0.0codValid4992018-08-01115325.0201882018-08-01
584519905204cod2018-08-28WOFSCE5AE00357AECDE699.01849.0Women's Fashion0.0codValid6992018-08-01115320.0201882018-08-01
584520905205processing2018-08-28MATHUA5AF70A7D1E50A35599.0135899.0Mobiles & Tablets0.0bankalfalahGross35,5992018-08-01115326.0201882018-08-01
584521905206processing2018-08-28MATSAM5B6D7208C6D30129999.02652178.0Mobiles & Tablets0.0bankalfalahGross259,9982018-07-01113474.0201882018-08-01
584522905207processing2018-08-28MATSAM5B1509B4696EA87300.02652178.0Mobiles & Tablets0.0bankalfalahGross174,6002018-07-01113474.0201882018-08-01
584523905208processing2018-08-28MATSAM5B10F91A9B6AB108640.02652178.0Mobiles & Tablets0.0bankalfalahGross217,2802018-07-01113474.0201882018-08-01